E-Book, Englisch, 602 Seiten
Reihe: Power Engineering (Willis)
E-Book, Englisch, 602 Seiten
Reihe: Power Engineering (Willis)
ISBN: 978-1-4200-6587-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
The book provides both the analytical formulation of optimization and various algorithmic issues that arise in the application of various methods in power system planning and operation. The second edition adds new functions involving market programs, pricing, reliability, and advances in intelligent systems with implemented algorithms and illustrative examples. It describes recent developments in the field of Adaptive Critics Design and practical applications of approximate dynamic programming. To round out the coverage, the final chapter combines fundamental theories and theorems from functional optimization, optimal control, and dynamic programming to explain new Adaptive Dynamic Programming concepts and variants.
With its one-of-a-kind integration of cornerstone optimization principles with application examples, this second edition propels power engineers to new discoveries in providing optimal supplies of energy.
Zielgruppe
Students, professors, and practicing engineers in signal processing or system engineering; applied mathematics; and decision makers.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction
Structure of a Generic Electric Power System
Power System Models
Power System Control
Power System Security Assessment
Power System Optimization as a Function of Time
Review of Optimization Techniques Applicable to Power Systems
Electric Power System Models
Complex Power Concepts
Three-Phase Systems
Per Unit Representation
Synchronous Machine Modeling
Reactive Capability Limits
Prime Movers and Governing Systems
Automatic Gain Control
Transmission Subsystems
Y-Bus Incorporating the Transformer Effect
Load Models
Available Transfer Capability
Illustrative Examples
Power-Flow Computations
Types of Buses for PF Studies
General Form of the PFEs
Practical Modeling Considerations
Iterative Techniques for PF Solution
Practical Applications of PF Studies
Illustrative Examples
Constrained Optimization and Applications
Theorems on the Optimization of Constrained Functions
Procedure for Optimizing Constrained Problems (Functions)
Karush–Kuhn Tucker Condition
Illustrative Problems
Power Systems Application Examples
Illustrative Examples
Linear Programming and Applications
Mathematical Model and Nomenclature in LP
LP Solution Techniques
Duality in LP
Khun–Tucker Conditions in LP
Mixed-Integer Programming
Sensitivity Methods for Postoptimization in LP
Power Systems Applications
Illustrative Examples
Interior Point Methods
Karmarkar’s Algorithm
Projective-Scaling Method
Dual Affine Algorithm
Primal Affine Algorithm
Barrier Algorithm
Extended IP Method for LP Problems
FI Sequence
Extended Quadratic Programming Using IP Method
Illustrative Examples
Nonlinear Programming
Classification of NLP Problems
Sensitivity Method for Solving NLP Variables
Algorithm for Quadratic Optimization
Illustrative Example (Barrier Method for Solving NLP)
Illustrative Examples
Dynamic Programming
Characteristics of DP
Concept of Suboptimization and the Principle of Optimality
Formulation of DP
Backward and Forward Recursion
Computational Procedure in DP
Computational Economy in DP
Systems with More Than One Constraint
Conversion of a Final Value Problem into an Initial Value Problem
Illustrative Examples
Lagrangian Relaxation
Concepts
Subgradient Method for Setting the Dual Variables
Setting tk
Comparison with LP-Based Bounds
Improved Relaxation
Summary of Concepts
Past Applications
Summary
Illustrative Examples
Decomposition Method
Formulation of the Decomposition Problem
Algorithm of the Decomposition Technique
Illustrative Example of the Decomposition Technique
State Estimation
Historical Perspective of State Estimation
Simple Mathematical Background
State Estimation Techniques
Applications to Power Network
Illustrative Examples
Optimal Power Flow
OPF—Fuel Cost Minimization
OPF—Active Power Loss Minimization
OPF—VAr Planning
OPF—Adding Environmental Constraints
Commonly Used Optimization Technique (LP)
Commonly Used Optimization Technique (NLP)
Illustrative Examples
Pricing
Marginal Pricing
Marginal Costing
Marginal Revenue
Pricing Policies for Regulated Systems and Markets
Pricing Methods
Economic Basis of Shadow Prices in Linear Programming (LP)
LMP
Alternative OPF Formulation for Pricing Using Duality in LP
Unit Commitment
Formulation of Unit Commitment
Optimization Methods
Illustrative Example
Updating ln(t) in the Unit Commitment Problem
Unit Commitment of Thermal Units Using Dynamic Programming
Illustrative Problems
Genetic Algorithms
Definition and Concepts Used in Genetic Computation
GA Approach
Theory of GAs
Schemata Theorem
General Algorithm of GAs
Application of GAs
Application to Power Systems
Illustrative Examples
Functional Optimization, Optimal Control, and Adaptive Dynamic Programming
System Performance Evaluation and Optimization of Functionals
Solving the Optimal Control Problem
Selected Methods of Determining the Control Functions for Convergence of Optimum Principle
Adaptive Critics Design (ACD) and ADP
Architecture of ACDs []
Typical Architectures of Variants or ADP (Critics Illustrations)
Applications of DSOPF to Power Systems Problems
Index
Each chapter includes an introduction, conclusion, and problem set
